Variationally Regularized Graph-based Representation Learning for Electronic Health Records

Weicheng Zhu (New York University) ; Narges Razavian (NYU Grossman School of Medicine)

Affinitention Nets: Kernel Perspective on Attention Architectures for Set Classification with Applications to Medical Text and Images

David Dov, Serge Assaad, Shijing Si, and Rui Wang (Duke University) ; Hongteng Xu (Renmin University of China) ; Shahar Ziv Kovalsky (UNC at Chapel Hill) ; Jonathan Bell and Danielle Elliott Range (Duke University Hospital) ; Jonathan Cohen (Kaplan Medical Center) ; Ricardo Henao and Lawrence Carin (Duke University)

Privacy-Preserving and Bandwidth-Efficient Federated Learning: An Application to In-Hospital Mortality Prediction

Raouf Kerkouche (Privatics team, Univ. Grenoble Alpes, Inria, 38000 Grenoble, France) ; Gergely Ács (Crysys Lab, BME-HIT) ; Claude Castelluccia (Privatics team, Univ. Grenoble Alpes, Inria, 38000 Grenoble, France) ; Pierre Genevès (Tyrex team Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LIG 38000 Grenoble, France)

Concept-based Model Explanations for Electronic Health Records

Diana Mincu (Google Research) ; Eric Loreaux (Google Health) ; Shaobo Hou (DeepMind) ; Sebastien Baur, Ivan Protsyuk, and Martin G Seneviratne (Google Health) ; Anne Mottram and Nenad Tomasev (DeepMind) ; Alan Karthikesalingam (Google Health) ; Jessica Schrouff (Google Research)

Trustworthy Machine Learning for Health Care: Scalable Data Valuation with the Shapley Value

Konstantin D Pandl, Fabian Feiland, Scott Thiebes, and Ali Sunyaev (Karlsruhe Institute of Technology)

Temporal Pointwise Convolutional Networks for Length of Stay Prediction in the Intensive Care Unit

Emma Rocheteau and Pietro Liò (University of Cambridge) ; Stephanie Hyland (Microsoft Research)

Self-supervised Transfer Learning of Physiological Representations from Free-living Wearable Data

Dimitris Spathis, Ignacio Pozuelo, Soren Brage, Nicholas J. Wareham, and Cecilia Mascolo (University of Cambridge)

Generative ODE Modeling with Known Unknowns

Ori Linial and Neta Ravid (Technion) ; Danny Eytan (Technion, Rambam) ; Uri Shalit (Technion)

Learning to Predict with Supporting Evidence: Applications to Clinical Risk Prediction

Aniruddh Raghu and John Guttag (Massachusetts Institute of Technology) ; Katherine Young (Harvard Medical School) ; Eugene Pomerantsev (Massachusetts General Hospital) ; Adrian V. Dalca (Harvard Medical School & MIT) ; Collin M. Stultz (Massachusetts Institute of Technology)

VisualCheXbert: Addressing the Discrepancy Between Radiology Report Labels and Image Labels

Saahil Jain and Akshay Smit (Stanford University) ; Steven QH Truong, Chanh DT Nguyen, and Minh-Thanh Huynh (VinBrain) ; Mudit Jain (unaffiliated) ; Victoria A. Young, Andrew Y. Ng, Matthew P. Lungren, and Pranav Rajpurkar (Stanford University)

CheXtransfer: Performance and Parameter Efficiency of ImageNet Models for Chest X-Ray Interpretation

Alexander Ke, William Ellsworth, Oishi Banerjee, Andrew Y. Ng, and Pranav Rajpurkar (Stanford University)

CheXternal: Generalization of Deep Learning Models for Chest X-ray Interpretation to Photos of Chest X-rays and External Clinical Settings

Pranav Rajpurkar, Anirudh Joshi, Anuj Pareek, Andrew Y. Ng, and Matthew P. Lungren (Stanford University)

Enabling Counterfactual Survival Analysis with Balanced Representations

Paidamoyo Chapfuwa, Serge Assaad, Shuxi Zeng, Michael Pencina, Lawrence Carin, and Ricardo Henao (Duke University)

Controlled Molecule Generator for Optimizing Multiple Chemical Properties

Bonggun Shin and Sungsoo Park (Deargen Inc.) ; JinYeong Bak (SungKyunKwan University) ; Joyce C. Ho (Emory University)

MetaPhys: Few-Shot Adaptation for Non-Contact Physiological Measurement

Xin Liu and Ziheng Jiang (University of Washington) ; Josh Fromm (OctoML) ; Xuhai Xu and Shwetak Patel (University of Washington) ; Daniel McDuff (Microsoft Research)

Learning to Safely Approve Updates to Machine Learning Algorithms

Jean Feng (University of California, San Francisco)

iGOS++: Integrated Gradient Optimized Saliency by Bilateral Perturbations

Saeed Khorram, Tyler Lawson, and Fuxin Li (Oregon State University)

Phenotypical Ontology Driven Framework for Multi-Task Learning

Mohamed Ghalwash, Zijun Yao, Prithwish Chakraborty, james Codella, and Daby Sow (IBM Research)

RNA Alternative Splicing Prediction with Discrete Compositional Energy Network

Alvin Chan, Anna Korsakova, Yew-Soon Ong, Fernaldo Richtia Winnerdy, Kah Wai Lim, and Anh Tuan Phan (Nanyang Technological University)

Predictive Models for Colorectal Cancer Recurrence Using Multi-modal Healthcare Data

Danliang Ho (National University of Singapore) ; Iain Bee Huat Tan (National Cancer Center Singapore) ; Mehul Motani (National University of Singapore)

B-SegNet : Branched-SegMentor Network For Skin Lesion Segmentation

shreshth saini (indian institute of technology jodhpur) ; Jeon Young Seok and Mengling Feng (Saw Swee Hock School of PublicHealth, National University HealthSystem, National University ofSingapore)

Modeling Longitudinal Dynamics of Comorbidities

Basil Maag, Stefan Feuerriegel, and Mathias Kraus (ETH Zurich) ; Maytal Saar-Tsechansky (University of Texas at Austin) ; Thomas Zueger (1) Inselspital, Bern, University Hospital, University of Bern 2) ETH Zurich)

T-DPSOM - An Interpretable Clustering Method for Unsupervised Learning of Patient Health States

Laura Manduchi, Matthias Hüser, Martin Faltys, Julia Vogt, Gunnar Rätsch, and Vincent Fortuin (ETH Zürich)

Contextualization and Individualization for Just-in-Time Adaptive Interventions to Reduce Sedentary Behavior

Matthew Saponaro, Ajith Vemuri, Greg Dominick, and Keith Decker (University of Delaware)

A Comprehensive EHR Timeseries Pre-training Benchmark

Matthew McDermott (Massachusetts Institute of Technology) ; Bret Nestor (University of Toronto) ; Evan Kim (Massachusetts Institute of Technology) ; Wancong Zhang (New York University) ; Anna Goldenberg (Hospital for Sick Children, University of Toronto, Vector Institute) ; Peter Szolovits (MIT) ; Marzyeh Ghassemi (University of Toronto ; Vector Institute for Artificial Intelligence)

An Empirical Framework for Domain Generalization In Clinical Settings

Haoran Zhang (University of Toronto ; Vector Institute) ; Natalie Dullerud (University of Toronto, Vector Institute) ; Laleh Seyyed-Kalantari (University of Toronto) ; Quaid Morris (Memorial Sloan Kettering Cancer Center) ; Shalmali Joshi (Harvard University) ; Marzyeh Ghassemi (University of Toronto ; Vector Institute for Artificial Intelligence)

Influenza-like Symptom Recognition using Mobile Sensing and Graph Neural Networks

Guimin Dong, Lihua Cai, Debajyoti Datta, Shashwat Kumar, Laura E. Barnes, and Mehdi Boukhechba (University of Virginia)

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